Here, we want to add edge weights to our graph embedding and see how that affects visualizations.
Use the reticulate package to use scVelo from within R:
Extract count data..
Filter genes
Downsample cells to make things easier
Normalize for dimensional reduction
## Warning in if (!class(counts) %in% c("dgCMatrix", "dgTMatrix")) {: the condition
## has length > 1 and only the first element will be used
## Converting to sparse matrix ...
## Normalizing matrix with 1232 cells and 8724 genes
Dimensional reduction
Run velocyto on panc data
Scores of observed and projected states in PC space
Graph visualization on subset of cells from PC coordinates
Here, I’m using the composite distances as edge weights.
Negative edge weights messes with the graph layout. In this case, because our similarity is 0.25, all of our composite distances are negative. So I took just took the abs value of composite distances. Probably need to figure out a better way to deal with negative edge weights…
Distribution of pairwise composite distance amongst all k nearest neighbors: